Explaining data patterns using knowledge from the web of data /:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Körperschaft: | |
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Amsterdam, Netherlands :
IOS Press,
[2018]
|
| Schriftenreihe: | Studies on the Semantic Web ;
vol. 034. |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Beschreibung: | 1 online resource |
| Bibliographie: | Includes bibliographical references. |
| ISBN: | 9781614998600 1614998604 |
Internformat
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| 505 | 0 | |a Intro; Title Page; Contents; Introduction and State of the Art; Introduction; Problem Statement; Research Hypothesis; Research Questions; RQ1: Definition of an Explanation; RQ2: Detection of the Background Knowledge; RQ3: Generation of the Explanations; RQ4: Evaluation of the Explanations; Research Methodology; Approach and Contributions; Applicability; Dedalo at a Glance; Contributions of the Thesis; Structure of the Thesis; Structure; Publications; Datasets and Use-cases; State of the Art; A Cognitive Science Perspective on Explanations; Characterisations of Explanations. | |
| 505 | 8 | |a The Explanation OntologyResearch Context; The Knowledge Discovery Process; Graph Terminology and Fundamentals; Historical Overview of the Web of Data; Consuming Knowledge from the Web of Data; Resources; Methods; Towards Knowledge Discovery from the Web of Data; Managing Graphs; Mining Graphs; Mining the Web of Data; Summary and Discussion; Looking for Pattern Explanations in the Web of Data; Manually generating Explanations; Introduction; The Inductive Logic Programming Framework; General Setting; Generic Technique; A Practical Example; The ILP Approach to Generate Explanations; Experiments. | |
| 505 | 8 | |a Building the Training ExamplesBuilding the Background Knowledge; Inducing Hypotheses; Discussion; Conclusions and Limitations; Automatically generating Explanations; Introduction; Problem Formalisation; Assumptions; Formal Definitions; An Example; Automatic Discovery of Explanations; Challenges and Proposed Solutions; Description of the Process; Evaluation Measures; Final Algorithm; Experiments; Use-cases; Heuristics Comparison; Best Explanations; Time Evaluation; Conclusions and Limitations; Aggregating Explanations using Neural Networks; Introduction; Motivation and Challenges. | |
| 505 | 8 | |a Improving Atomic RulesRule Interestingness Measures; Neural Networks to Predict Combinations; Proposed Approach; A Neural Network Model to Predict Aggregations; Integrating the Model in Dedalo; Experiments; Comparing Strategies for Rule Aggregation; Results and Discussion; Conclusions and Limitations; Contextualising Explanations with the Web of Data; Introduction; Problem Statement; Learning Path Evaluation Functions through Genetic Programming; Genetic Programming Foundations; Preparatory Steps; Step-by-Step Run; Experiments; Experimental Setting; Results; Conclusion and Limitations. | |
| 505 | 8 | |a Evaluation and ConclusionEvaluating Dedalo with Google Trends; Introduction; First Empirical Study; Data Preparation; Evaluation Interface; Evaluation Measurements; Participant Details; User Agreement; Results, Discussion and Error Analysis; Second Empirical Study; Data Preparation; Evaluation Interface; Evaluation Measurements; User Agreement; Results, Discussion and Error Analysis; Final Discussion and Conclusions; Discussion and Conclusions; Introduction; Summary, Answers and Contributions; Definition of an Explanation; Detection of the Background Knowledge; Generation of the Explanations. | |
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| contents | Intro; Title Page; Contents; Introduction and State of the Art; Introduction; Problem Statement; Research Hypothesis; Research Questions; RQ1: Definition of an Explanation; RQ2: Detection of the Background Knowledge; RQ3: Generation of the Explanations; RQ4: Evaluation of the Explanations; Research Methodology; Approach and Contributions; Applicability; Dedalo at a Glance; Contributions of the Thesis; Structure of the Thesis; Structure; Publications; Datasets and Use-cases; State of the Art; A Cognitive Science Perspective on Explanations; Characterisations of Explanations. The Explanation OntologyResearch Context; The Knowledge Discovery Process; Graph Terminology and Fundamentals; Historical Overview of the Web of Data; Consuming Knowledge from the Web of Data; Resources; Methods; Towards Knowledge Discovery from the Web of Data; Managing Graphs; Mining Graphs; Mining the Web of Data; Summary and Discussion; Looking for Pattern Explanations in the Web of Data; Manually generating Explanations; Introduction; The Inductive Logic Programming Framework; General Setting; Generic Technique; A Practical Example; The ILP Approach to Generate Explanations; Experiments. Building the Training ExamplesBuilding the Background Knowledge; Inducing Hypotheses; Discussion; Conclusions and Limitations; Automatically generating Explanations; Introduction; Problem Formalisation; Assumptions; Formal Definitions; An Example; Automatic Discovery of Explanations; Challenges and Proposed Solutions; Description of the Process; Evaluation Measures; Final Algorithm; Experiments; Use-cases; Heuristics Comparison; Best Explanations; Time Evaluation; Conclusions and Limitations; Aggregating Explanations using Neural Networks; Introduction; Motivation and Challenges. Improving Atomic RulesRule Interestingness Measures; Neural Networks to Predict Combinations; Proposed Approach; A Neural Network Model to Predict Aggregations; Integrating the Model in Dedalo; Experiments; Comparing Strategies for Rule Aggregation; Results and Discussion; Conclusions and Limitations; Contextualising Explanations with the Web of Data; Introduction; Problem Statement; Learning Path Evaluation Functions through Genetic Programming; Genetic Programming Foundations; Preparatory Steps; Step-by-Step Run; Experiments; Experimental Setting; Results; Conclusion and Limitations. Evaluation and ConclusionEvaluating Dedalo with Google Trends; Introduction; First Empirical Study; Data Preparation; Evaluation Interface; Evaluation Measurements; Participant Details; User Agreement; Results, Discussion and Error Analysis; Second Empirical Study; Data Preparation; Evaluation Interface; Evaluation Measurements; User Agreement; Results, Discussion and Error Analysis; Final Discussion and Conclusions; Discussion and Conclusions; Introduction; Summary, Answers and Contributions; Definition of an Explanation; Detection of the Background Knowledge; Generation of the Explanations. |
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| spelling | Tiddi, Ilaria, author. Explaining data patterns using knowledge from the web of data / Ilaria Tiddi. Amsterdam, Netherlands : IOS Press, [2018] 1 online resource text txt rdacontent computer c rdamedia online resource cr rdacarrier Studies on the semantic web ; vol. 034 Includes bibliographical references. Online record; title from digital title page (viewed on October 29, 2018). Intro; Title Page; Contents; Introduction and State of the Art; Introduction; Problem Statement; Research Hypothesis; Research Questions; RQ1: Definition of an Explanation; RQ2: Detection of the Background Knowledge; RQ3: Generation of the Explanations; RQ4: Evaluation of the Explanations; Research Methodology; Approach and Contributions; Applicability; Dedalo at a Glance; Contributions of the Thesis; Structure of the Thesis; Structure; Publications; Datasets and Use-cases; State of the Art; A Cognitive Science Perspective on Explanations; Characterisations of Explanations. The Explanation OntologyResearch Context; The Knowledge Discovery Process; Graph Terminology and Fundamentals; Historical Overview of the Web of Data; Consuming Knowledge from the Web of Data; Resources; Methods; Towards Knowledge Discovery from the Web of Data; Managing Graphs; Mining Graphs; Mining the Web of Data; Summary and Discussion; Looking for Pattern Explanations in the Web of Data; Manually generating Explanations; Introduction; The Inductive Logic Programming Framework; General Setting; Generic Technique; A Practical Example; The ILP Approach to Generate Explanations; Experiments. Building the Training ExamplesBuilding the Background Knowledge; Inducing Hypotheses; Discussion; Conclusions and Limitations; Automatically generating Explanations; Introduction; Problem Formalisation; Assumptions; Formal Definitions; An Example; Automatic Discovery of Explanations; Challenges and Proposed Solutions; Description of the Process; Evaluation Measures; Final Algorithm; Experiments; Use-cases; Heuristics Comparison; Best Explanations; Time Evaluation; Conclusions and Limitations; Aggregating Explanations using Neural Networks; Introduction; Motivation and Challenges. Improving Atomic RulesRule Interestingness Measures; Neural Networks to Predict Combinations; Proposed Approach; A Neural Network Model to Predict Aggregations; Integrating the Model in Dedalo; Experiments; Comparing Strategies for Rule Aggregation; Results and Discussion; Conclusions and Limitations; Contextualising Explanations with the Web of Data; Introduction; Problem Statement; Learning Path Evaluation Functions through Genetic Programming; Genetic Programming Foundations; Preparatory Steps; Step-by-Step Run; Experiments; Experimental Setting; Results; Conclusion and Limitations. Evaluation and ConclusionEvaluating Dedalo with Google Trends; Introduction; First Empirical Study; Data Preparation; Evaluation Interface; Evaluation Measurements; Participant Details; User Agreement; Results, Discussion and Error Analysis; Second Empirical Study; Data Preparation; Evaluation Interface; Evaluation Measurements; User Agreement; Results, Discussion and Error Analysis; Final Discussion and Conclusions; Discussion and Conclusions; Introduction; Summary, Answers and Contributions; Definition of an Explanation; Detection of the Background Knowledge; Generation of the Explanations. Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Data Mining https://id.nlm.nih.gov/mesh/D057225 Exploration de données (Informatique) COMPUTERS General. bisacsh Data mining fast IOS Press. http://id.loc.gov/authorities/names/no2015091156 Print version: Ilaria, Tiddi. Explaining data patterns using knowledge from the web of data. Amsterdam, Netherlands : IOS Press, [2018] 9781614998594 (OCoLC)1045491790 Studies on the Semantic Web ; vol. 034. http://id.loc.gov/authorities/names/no2009156151 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1876770 Volltext CBO01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1876770 Volltext |
| spellingShingle | Tiddi, Ilaria Explaining data patterns using knowledge from the web of data / Studies on the Semantic Web ; Intro; Title Page; Contents; Introduction and State of the Art; Introduction; Problem Statement; Research Hypothesis; Research Questions; RQ1: Definition of an Explanation; RQ2: Detection of the Background Knowledge; RQ3: Generation of the Explanations; RQ4: Evaluation of the Explanations; Research Methodology; Approach and Contributions; Applicability; Dedalo at a Glance; Contributions of the Thesis; Structure of the Thesis; Structure; Publications; Datasets and Use-cases; State of the Art; A Cognitive Science Perspective on Explanations; Characterisations of Explanations. The Explanation OntologyResearch Context; The Knowledge Discovery Process; Graph Terminology and Fundamentals; Historical Overview of the Web of Data; Consuming Knowledge from the Web of Data; Resources; Methods; Towards Knowledge Discovery from the Web of Data; Managing Graphs; Mining Graphs; Mining the Web of Data; Summary and Discussion; Looking for Pattern Explanations in the Web of Data; Manually generating Explanations; Introduction; The Inductive Logic Programming Framework; General Setting; Generic Technique; A Practical Example; The ILP Approach to Generate Explanations; Experiments. Building the Training ExamplesBuilding the Background Knowledge; Inducing Hypotheses; Discussion; Conclusions and Limitations; Automatically generating Explanations; Introduction; Problem Formalisation; Assumptions; Formal Definitions; An Example; Automatic Discovery of Explanations; Challenges and Proposed Solutions; Description of the Process; Evaluation Measures; Final Algorithm; Experiments; Use-cases; Heuristics Comparison; Best Explanations; Time Evaluation; Conclusions and Limitations; Aggregating Explanations using Neural Networks; Introduction; Motivation and Challenges. Improving Atomic RulesRule Interestingness Measures; Neural Networks to Predict Combinations; Proposed Approach; A Neural Network Model to Predict Aggregations; Integrating the Model in Dedalo; Experiments; Comparing Strategies for Rule Aggregation; Results and Discussion; Conclusions and Limitations; Contextualising Explanations with the Web of Data; Introduction; Problem Statement; Learning Path Evaluation Functions through Genetic Programming; Genetic Programming Foundations; Preparatory Steps; Step-by-Step Run; Experiments; Experimental Setting; Results; Conclusion and Limitations. Evaluation and ConclusionEvaluating Dedalo with Google Trends; Introduction; First Empirical Study; Data Preparation; Evaluation Interface; Evaluation Measurements; Participant Details; User Agreement; Results, Discussion and Error Analysis; Second Empirical Study; Data Preparation; Evaluation Interface; Evaluation Measurements; User Agreement; Results, Discussion and Error Analysis; Final Discussion and Conclusions; Discussion and Conclusions; Introduction; Summary, Answers and Contributions; Definition of an Explanation; Detection of the Background Knowledge; Generation of the Explanations. Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Data Mining https://id.nlm.nih.gov/mesh/D057225 Exploration de données (Informatique) COMPUTERS General. bisacsh Data mining fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh97002073 https://id.nlm.nih.gov/mesh/D057225 |
| title | Explaining data patterns using knowledge from the web of data / |
| title_auth | Explaining data patterns using knowledge from the web of data / |
| title_exact_search | Explaining data patterns using knowledge from the web of data / |
| title_full | Explaining data patterns using knowledge from the web of data / Ilaria Tiddi. |
| title_fullStr | Explaining data patterns using knowledge from the web of data / Ilaria Tiddi. |
| title_full_unstemmed | Explaining data patterns using knowledge from the web of data / Ilaria Tiddi. |
| title_short | Explaining data patterns using knowledge from the web of data / |
| title_sort | explaining data patterns using knowledge from the web of data |
| topic | Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Data Mining https://id.nlm.nih.gov/mesh/D057225 Exploration de données (Informatique) COMPUTERS General. bisacsh Data mining fast |
| topic_facet | Data mining. Data Mining Exploration de données (Informatique) COMPUTERS General. Data mining |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1876770 |
| work_keys_str_mv | AT tiddiilaria explainingdatapatternsusingknowledgefromthewebofdata AT iospress explainingdatapatternsusingknowledgefromthewebofdata |